A feasibility study of a computational modeling system for performance evaluation and development of ultrasound strain elastography systems
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Ultrasound strain elastography (USE) is an imaging technology that enables us to detect changes in tissue stiffness resulting from cancer and other diseases. The objective of this study is to computationally model the application of USE for breast lesion characterization. We develop a well-defined simulation pipeline using open-source software to create in silico USE phantoms with one and two stiff targets. First, we use FreeCAD software for tissue 3D modeling and Gmsh software for finite element (FE) meshes. Second, we place randomly positioned point scatterers within the meshed models to form pre-deformation virtual ultrasound phantoms. Then, a simulated ultrasound transducer is used to compress and deform tissue in FE simulations using FEBio software to create a post-deformation virtual ultrasound phantom. Third, we use the k-Wave acoustics toolbox to generate pre- and post-deformation ultrasound echo signals and B-mode images. Finally, we estimate axial and lateral displacements using a speckle tracking method, and strain elastograms, using a least-squares method. Displacements from the USE simulation pipeline and phantom experiments were compared against true FEBio-simulated displacements for accuracy. We have also quantitatively compared the resultant strain elastograms obtained from FEBio simulations, USE simulation pipeline, and phantom experiments. Finally, model validation is performed by comparing the performance of the USE software platform and physical phantom experiments for a range of compression values (0.5%–5% axial strain). The results confirm the use of the well-validated USE simulation pipeline as a robust non-clinical assessment tool for USE system development.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it